Vulnerability assessment modelling for railway networks

Railway networks are prone to many different potential disruptive events such as technical failures (e.g. the failure of aging components), natural disasters (e.g. flooding) and intentional man-made disasters (e.g. trespass and suicide). Assessing the vulnerability of railway networks can help infrastructure managers to create the right preventive strategies to improve the robustness and the resilience of railway networks before the occurrence of disruptions. This study proposes a stochastic-vulnerability analysis model that enables the critical components of railway networks to be identified. The model is developed using a discrete event simulation technique. Its framework includes modules for assigning the disruption to the network components, predicting the network vulnerability, in terms of passenger delays and journey cancellations, and calculating the risk-based criticality of network components. Finally, an example application of the model is presented using a part of the East Midland railway network in UK

Vulnerability assessment modelling for railway networks

Railway networks are prone to many different potential disruptive events such as technical failures (e.g. the failure of aging components), natural disasters (e.g. flooding) and intentional man-made disasters (e.g. trespass and suicide). Assessing the vulnerability of railway networks can help infrastructure managers to create the right preventive strategies to improve the robustness and the resilience of railway networks before the occurrence of disruptions. This study proposes a stochastic-vulnerability analysis model that enables the critical components of railway networks to be identified. The model is developed using a discrete event simulation technique. Its framework includes modules for assigning the disruption to the network components, predicting the network vulnerability, in terms of passenger delays and journey cancellations, and calculating the risk-based criticality of network components. Finally, an example application of the model is presented using a part of the East Midland railway network in UK